通过主动推理增强基于种群的搜索
Enhancing Population-based Search with Active Inference
AI总结：
针对传统基于种群的元启发式算法缺乏预期性适应的问题，提出将主动推理框架与蚁群优化结合，在旅行商问题上验证发现仅小幅增加计算成本即可获得部分更优解。
摘要：
主动推理（Active Inference）框架将感知与行动建模为一个统一过程，智能体利用概率模型进行预测，并主动最小化感官差异。作为补充与对比，传统基于种群的元启发式算法依赖反应式的环境交互，不具备预期性适应能力。本文提出将主动推理整合到这类元启发式算法中，通过预期性环境适应提升性能。我们专门结合蚁群优化（Ant Colony Optimization, ACO）在旅行商问题（Travelling Salesman Problem, TSP）上验证了该方法。实验结果表明，主动推理仅需小幅增加计算成本就能得到部分更优解，其性能表现的有趣模式与图中节点的数量和拓扑结构相关。未来工作将明确不同类型的主动推理增强种群元启发式算法在何种场景、何时能发挥效用。
英文摘要：
The Active Inference framework models perception and action as a unified process, where agents use probabilistic models to predict and actively minimize sensory discrepancies. In complement and contrast, traditional population-based metaheuristics rely on reactive environmental interactions without anticipatory adaptation. This paper proposes the integration of Active Inference into these metaheuristics to enhance performance through anticipatory environmental adaptation. We demonstrate this approach specifically with Ant Colony Optimization (ACO) on the Travelling Salesman Problem (TSP). Experimental results indicate that Active Inference can yield some improved solutions with only a marginal increase in computational cost, with interesting patterns of performance that relate to number and topology of nodes in the graph. Further work will characterize where and when different types of Active Inference augmentation of population metaheuristics may be efficacious.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
